We present an efficient system for video search that maximizes the use of human bandwidth, while at the same time exploiting the machine’s ability to learn in real-time from use...
Alexander G. Hauptmann, Wei-Hao Lin, Rong Yan, Jun...
We consider the problem of recognizing human actions from still images. We propose a novel approach that treats the pose of the person in the image as latent variables that will h...
We consider the fully automated recognition of actions in uncontrolled environment. Most existing work relies on domain knowledge to construct complex handcrafted features from in...
In this paper a framework “Temporal-Vector Trajectory Learning” (TVTL) for human action recognition is proposed. In this framework, the major concept is that we would like to a...
This work proposes to learn visual encodings of attention patterns that enables sequential attention for object detection in real world environments. The system embeds a saccadic d...